Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations8950
Missing cells314
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory191.0 B

Variable types

Text1
Numeric17

Alerts

BALANCE is highly overall correlated with BALANCE_FREQUENCY and 4 other fieldsHigh correlation
BALANCE_FREQUENCY is highly overall correlated with BALANCE and 1 other fieldsHigh correlation
CASH_ADVANCE is highly overall correlated with BALANCE and 2 other fieldsHigh correlation
CASH_ADVANCE_FREQUENCY is highly overall correlated with BALANCE and 2 other fieldsHigh correlation
CASH_ADVANCE_TRX is highly overall correlated with BALANCE and 2 other fieldsHigh correlation
INSTALLMENTS_PURCHASES is highly overall correlated with PURCHASES and 3 other fieldsHigh correlation
MINIMUM_PAYMENTS is highly overall correlated with BALANCE and 1 other fieldsHigh correlation
ONEOFF_PURCHASES is highly overall correlated with ONEOFF_PURCHASES_FREQUENCY and 2 other fieldsHigh correlation
ONEOFF_PURCHASES_FREQUENCY is highly overall correlated with ONEOFF_PURCHASES and 2 other fieldsHigh correlation
PURCHASES is highly overall correlated with INSTALLMENTS_PURCHASES and 5 other fieldsHigh correlation
PURCHASES_FREQUENCY is highly overall correlated with INSTALLMENTS_PURCHASES and 3 other fieldsHigh correlation
PURCHASES_INSTALLMENTS_FREQUENCY is highly overall correlated with INSTALLMENTS_PURCHASES and 3 other fieldsHigh correlation
PURCHASES_TRX is highly overall correlated with INSTALLMENTS_PURCHASES and 5 other fieldsHigh correlation
MINIMUM_PAYMENTS has 313 (3.5%) missing valuesMissing
CUST_ID has unique valuesUnique
PURCHASES has 2044 (22.8%) zerosZeros
ONEOFF_PURCHASES has 4302 (48.1%) zerosZeros
INSTALLMENTS_PURCHASES has 3916 (43.8%) zerosZeros
CASH_ADVANCE has 4628 (51.7%) zerosZeros
PURCHASES_FREQUENCY has 2043 (22.8%) zerosZeros
ONEOFF_PURCHASES_FREQUENCY has 4302 (48.1%) zerosZeros
PURCHASES_INSTALLMENTS_FREQUENCY has 3915 (43.7%) zerosZeros
CASH_ADVANCE_FREQUENCY has 4628 (51.7%) zerosZeros
CASH_ADVANCE_TRX has 4628 (51.7%) zerosZeros
PURCHASES_TRX has 2044 (22.8%) zerosZeros
PAYMENTS has 240 (2.7%) zerosZeros
PRC_FULL_PAYMENT has 5903 (66.0%) zerosZeros

Reproduction

Analysis started2024-07-31 09:52:15.065385
Analysis finished2024-07-31 09:52:32.485787
Duration17.42 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

CUST_ID
Text

UNIQUE 

Distinct8950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size480.8 KiB
2024-07-31T13:52:32.682663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters53700
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8950 ?
Unique (%)100.0%

Sample

1st rowC10001
2nd rowC10002
3rd rowC10003
4th rowC10004
5th rowC10005
ValueCountFrequency (%)
c10001 1
 
< 0.1%
c10011 1
 
< 0.1%
c10017 1
 
< 0.1%
c10016 1
 
< 0.1%
c10015 1
 
< 0.1%
c10014 1
 
< 0.1%
c10013 1
 
< 0.1%
c10012 1
 
< 0.1%
c10010 1
 
< 0.1%
c10034 1
 
< 0.1%
Other values (8940) 8940
99.9%
2024-07-31T13:52:32.881569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12672
23.6%
C 8950
16.7%
0 3737
 
7.0%
2 3652
 
6.8%
3 3651
 
6.8%
5 3642
 
6.8%
7 3640
 
6.8%
4 3636
 
6.8%
6 3633
 
6.8%
8 3633
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 12672
23.6%
C 8950
16.7%
0 3737
 
7.0%
2 3652
 
6.8%
3 3651
 
6.8%
5 3642
 
6.8%
7 3640
 
6.8%
4 3636
 
6.8%
6 3633
 
6.8%
8 3633
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 12672
23.6%
C 8950
16.7%
0 3737
 
7.0%
2 3652
 
6.8%
3 3651
 
6.8%
5 3642
 
6.8%
7 3640
 
6.8%
4 3636
 
6.8%
6 3633
 
6.8%
8 3633
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 12672
23.6%
C 8950
16.7%
0 3737
 
7.0%
2 3652
 
6.8%
3 3651
 
6.8%
5 3642
 
6.8%
7 3640
 
6.8%
4 3636
 
6.8%
6 3633
 
6.8%
8 3633
 
6.8%

BALANCE
Real number (ℝ)

HIGH CORRELATION 

Distinct8871
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1564.4748
Minimum0
Maximum19043.139
Zeros80
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2024-07-31T13:52:33.116945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.8145184
Q1128.28192
median873.38523
Q32054.14
95-th percentile5909.1118
Maximum19043.139
Range19043.139
Interquartile range (IQR)1925.8581

Descriptive statistics

Standard deviation2081.5319
Coefficient of variation (CV)1.3304988
Kurtosis7.6747513
Mean1564.4748
Median Absolute Deviation (MAD)799.8652
Skewness2.393386
Sum14002050
Variance4332775
MonotonicityNot monotonic
2024-07-31T13:52:33.181135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 80
 
0.9%
40.900749 1
 
< 0.1%
1213.551338 1
 
< 0.1%
1253.188317 1
 
< 0.1%
5058.299635 1
 
< 0.1%
296.905944 1
 
< 0.1%
1084.652647 1
 
< 0.1%
237.198442 1
 
< 0.1%
1636.518315 1
 
< 0.1%
468.851415 1
 
< 0.1%
Other values (8861) 8861
99.0%
ValueCountFrequency (%)
0 80
0.9%
0.000199 1
 
< 0.1%
0.001146 1
 
< 0.1%
0.001214 1
 
< 0.1%
0.001289 1
 
< 0.1%
0.004816 1
 
< 0.1%
0.006651 1
 
< 0.1%
0.009684 1
 
< 0.1%
0.01968 1
 
< 0.1%
0.021102 1
 
< 0.1%
ValueCountFrequency (%)
19043.13856 1
< 0.1%
18495.55855 1
< 0.1%
16304.88925 1
< 0.1%
16259.44857 1
< 0.1%
16115.5964 1
< 0.1%
15532.33972 1
< 0.1%
15258.2259 1
< 0.1%
15244.74865 1
< 0.1%
15155.53286 1
< 0.1%
14581.45914 1
< 0.1%

BALANCE_FREQUENCY
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.87727073
Minimum0
Maximum1
Zeros80
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2024-07-31T13:52:33.248719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.272727
Q10.888889
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.111111

Descriptive statistics

Standard deviation0.236904
Coefficient of variation (CV)0.27004663
Kurtosis3.0923696
Mean0.87727073
Median Absolute Deviation (MAD)0
Skewness-2.0232655
Sum7851.573
Variance0.056123506
MonotonicityNot monotonic
2024-07-31T13:52:33.315514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 6211
69.4%
0.909091 410
 
4.6%
0.818182 278
 
3.1%
0.727273 223
 
2.5%
0.545455 219
 
2.4%
0.636364 209
 
2.3%
0.454545 172
 
1.9%
0.363636 170
 
1.9%
0.272727 151
 
1.7%
0.181818 146
 
1.6%
Other values (33) 761
 
8.5%
ValueCountFrequency (%)
0 80
0.9%
0.090909 67
0.7%
0.1 8
 
0.1%
0.111111 5
 
0.1%
0.125 9
 
0.1%
0.142857 7
 
0.1%
0.166667 7
 
0.1%
0.181818 146
1.6%
0.2 9
 
0.1%
0.222222 5
 
0.1%
ValueCountFrequency (%)
1 6211
69.4%
0.909091 410
 
4.6%
0.9 55
 
0.6%
0.888889 53
 
0.6%
0.875 57
 
0.6%
0.857143 51
 
0.6%
0.833333 60
 
0.7%
0.818182 278
 
3.1%
0.8 20
 
0.2%
0.777778 22
 
0.2%

PURCHASES
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6203
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1003.2048
Minimum0
Maximum49039.57
Zeros2044
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2024-07-31T13:52:33.379204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139.635
median361.28
Q31110.13
95-th percentile3998.6195
Maximum49039.57
Range49039.57
Interquartile range (IQR)1070.495

Descriptive statistics

Standard deviation2136.6348
Coefficient of variation (CV)2.1298091
Kurtosis111.38877
Mean1003.2048
Median Absolute Deviation (MAD)361.28
Skewness8.1442691
Sum8978683.3
Variance4565208.2
MonotonicityNot monotonic
2024-07-31T13:52:33.444919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2044
 
22.8%
45.65 27
 
0.3%
60 16
 
0.2%
150 16
 
0.2%
300 13
 
0.1%
200 13
 
0.1%
100 13
 
0.1%
450 12
 
0.1%
50 10
 
0.1%
600 10
 
0.1%
Other values (6193) 6776
75.7%
ValueCountFrequency (%)
0 2044
22.8%
0.01 4
 
< 0.1%
0.05 1
 
< 0.1%
0.24 1
 
< 0.1%
0.7 1
 
< 0.1%
1 2
 
< 0.1%
1.4 1
 
< 0.1%
2 1
 
< 0.1%
4.44 1
 
< 0.1%
4.8 1
 
< 0.1%
ValueCountFrequency (%)
49039.57 1
< 0.1%
41050.4 1
< 0.1%
40040.71 1
< 0.1%
38902.71 1
< 0.1%
35131.16 1
< 0.1%
32539.78 1
< 0.1%
31299.35 1
< 0.1%
27957.68 1
< 0.1%
27790.42 1
< 0.1%
26784.62 1
< 0.1%

ONEOFF_PURCHASES
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4014
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean592.43737
Minimum0
Maximum40761.25
Zeros4302
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2024-07-31T13:52:33.508669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median38
Q3577.405
95-th percentile2671.094
Maximum40761.25
Range40761.25
Interquartile range (IQR)577.405

Descriptive statistics

Standard deviation1659.8879
Coefficient of variation (CV)2.8017948
Kurtosis164.18757
Mean592.43737
Median Absolute Deviation (MAD)38
Skewness10.045083
Sum5302314.5
Variance2755227.9
MonotonicityNot monotonic
2024-07-31T13:52:33.586019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4302
48.1%
45.65 46
 
0.5%
50 17
 
0.2%
200 15
 
0.2%
60 13
 
0.1%
100 13
 
0.1%
150 12
 
0.1%
70 12
 
0.1%
1000 12
 
0.1%
250 11
 
0.1%
Other values (4004) 4497
50.2%
ValueCountFrequency (%)
0 4302
48.1%
0.01 7
 
0.1%
0.02 2
 
< 0.1%
0.05 1
 
< 0.1%
0.24 1
 
< 0.1%
0.7 1
 
< 0.1%
1 4
 
< 0.1%
1.4 2
 
< 0.1%
2 1
 
< 0.1%
4.99 1
 
< 0.1%
ValueCountFrequency (%)
40761.25 1
< 0.1%
40624.06 1
< 0.1%
34087.73 1
< 0.1%
33803.84 1
< 0.1%
26547.43 1
< 0.1%
26514.32 1
< 0.1%
25122.77 1
< 0.1%
24543.52 1
< 0.1%
23032.97 1
< 0.1%
22257.39 1
< 0.1%

INSTALLMENTS_PURCHASES
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4452
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411.06764
Minimum0
Maximum22500
Zeros3916
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2024-07-31T13:52:33.663289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median89
Q3468.6375
95-th percentile1750.0875
Maximum22500
Range22500
Interquartile range (IQR)468.6375

Descriptive statistics

Standard deviation904.33812
Coefficient of variation (CV)2.199974
Kurtosis96.575178
Mean411.06764
Median Absolute Deviation (MAD)89
Skewness7.2991199
Sum3679055.4
Variance817827.43
MonotonicityNot monotonic
2024-07-31T13:52:34.098639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3916
43.8%
300 14
 
0.2%
200 14
 
0.2%
100 14
 
0.2%
150 12
 
0.1%
125 11
 
0.1%
75 9
 
0.1%
350 8
 
0.1%
450 8
 
0.1%
500 8
 
0.1%
Other values (4442) 4936
55.2%
ValueCountFrequency (%)
0 3916
43.8%
1.95 1
 
< 0.1%
4.44 1
 
< 0.1%
4.8 1
 
< 0.1%
6.33 1
 
< 0.1%
7.26 1
 
< 0.1%
7.67 1
 
< 0.1%
9.28 1
 
< 0.1%
9.58 1
 
< 0.1%
9.65 1
 
< 0.1%
ValueCountFrequency (%)
22500 1
< 0.1%
15497.19 1
< 0.1%
14686.1 1
< 0.1%
13184.43 1
< 0.1%
12738.47 1
< 0.1%
12560.85 1
< 0.1%
12541 1
< 0.1%
12375 1
< 0.1%
12235.05 1
< 0.1%
12128.94 1
< 0.1%

CASH_ADVANCE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4323
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean978.87111
Minimum0
Maximum47137.212
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2024-07-31T13:52:34.216410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31113.8211
95-th percentile4647.1691
Maximum47137.212
Range47137.212
Interquartile range (IQR)1113.8211

Descriptive statistics

Standard deviation2097.1639
Coefficient of variation (CV)2.1424311
Kurtosis52.899434
Mean978.87111
Median Absolute Deviation (MAD)0
Skewness5.1666091
Sum8760896.5
Variance4398096.3
MonotonicityNot monotonic
2024-07-31T13:52:34.288104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4628
51.7%
495.425832 1
 
< 0.1%
1486.243293 1
 
< 0.1%
855.232779 1
 
< 0.1%
3767.104707 1
 
< 0.1%
291.608512 1
 
< 0.1%
38.690552 1
 
< 0.1%
521.664369 1
 
< 0.1%
1974.202963 1
 
< 0.1%
2462.100789 1
 
< 0.1%
Other values (4313) 4313
48.2%
ValueCountFrequency (%)
0 4628
51.7%
14.222216 1
 
< 0.1%
18.042768 1
 
< 0.1%
18.117967 1
 
< 0.1%
18.123413 1
 
< 0.1%
18.126683 1
 
< 0.1%
18.149946 1
 
< 0.1%
18.204577 1
 
< 0.1%
18.240626 1
 
< 0.1%
18.280043 1
 
< 0.1%
ValueCountFrequency (%)
47137.21176 1
< 0.1%
29282.10915 1
< 0.1%
27296.48576 1
< 0.1%
26268.69989 1
< 0.1%
26194.04954 1
< 0.1%
23130.82106 1
< 0.1%
22665.7785 1
< 0.1%
21943.84942 1
< 0.1%
20712.67008 1
< 0.1%
20277.33112 1
< 0.1%

PURCHASES_FREQUENCY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49035055
Minimum0
Maximum1
Zeros2043
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2024-07-31T13:52:34.350906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.083333
median0.5
Q30.916667
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.833334

Descriptive statistics

Standard deviation0.40137075
Coefficient of variation (CV)0.81853839
Kurtosis-1.6386309
Mean0.49035055
Median Absolute Deviation (MAD)0.416667
Skewness0.060164236
Sum4388.6374
Variance0.16109848
MonotonicityNot monotonic
2024-07-31T13:52:34.428287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 2178
24.3%
0 2043
22.8%
0.083333 677
 
7.6%
0.916667 396
 
4.4%
0.5 395
 
4.4%
0.166667 392
 
4.4%
0.833333 373
 
4.2%
0.333333 367
 
4.1%
0.25 345
 
3.9%
0.583333 316
 
3.5%
Other values (37) 1468
16.4%
ValueCountFrequency (%)
0 2043
22.8%
0.083333 677
 
7.6%
0.090909 43
 
0.5%
0.1 27
 
0.3%
0.111111 18
 
0.2%
0.125 32
 
0.4%
0.142857 26
 
0.3%
0.166667 392
 
4.4%
0.181818 16
 
0.2%
0.2 19
 
0.2%
ValueCountFrequency (%)
1 2178
24.3%
0.916667 396
 
4.4%
0.909091 28
 
0.3%
0.9 24
 
0.3%
0.888889 18
 
0.2%
0.875 26
 
0.3%
0.857143 25
 
0.3%
0.833333 373
 
4.2%
0.818182 21
 
0.2%
0.8 9
 
0.1%

ONEOFF_PURCHASES_FREQUENCY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20245768
Minimum0
Maximum1
Zeros4302
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2024-07-31T13:52:34.491895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.083333
Q30.3
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.29833607
Coefficient of variation (CV)1.4735725
Kurtosis1.1618456
Mean0.20245768
Median Absolute Deviation (MAD)0.083333
Skewness1.5356128
Sum1811.9963
Variance0.089004408
MonotonicityNot monotonic
2024-07-31T13:52:34.555440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 4302
48.1%
0.083333 1104
 
12.3%
0.166667 592
 
6.6%
1 481
 
5.4%
0.25 418
 
4.7%
0.333333 355
 
4.0%
0.416667 244
 
2.7%
0.5 235
 
2.6%
0.583333 197
 
2.2%
0.666667 167
 
1.9%
Other values (37) 855
 
9.6%
ValueCountFrequency (%)
0 4302
48.1%
0.083333 1104
 
12.3%
0.090909 56
 
0.6%
0.1 39
 
0.4%
0.111111 26
 
0.3%
0.125 41
 
0.5%
0.142857 37
 
0.4%
0.166667 592
 
6.6%
0.181818 34
 
0.4%
0.2 27
 
0.3%
ValueCountFrequency (%)
1 481
5.4%
0.916667 151
 
1.7%
0.909091 4
 
< 0.1%
0.9 1
 
< 0.1%
0.888889 2
 
< 0.1%
0.875 6
 
0.1%
0.857143 1
 
< 0.1%
0.833333 120
 
1.3%
0.818182 10
 
0.1%
0.8 4
 
< 0.1%

PURCHASES_INSTALLMENTS_FREQUENCY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36443734
Minimum0
Maximum1
Zeros3915
Zeros (%)43.7%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2024-07-31T13:52:34.617905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.166667
Q30.75
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.39744778
Coefficient of variation (CV)1.0905792
Kurtosis-1.3986322
Mean0.36443734
Median Absolute Deviation (MAD)0.166667
Skewness0.50920116
Sum3261.7142
Variance0.15796474
MonotonicityNot monotonic
2024-07-31T13:52:34.680222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 3915
43.7%
1 1331
 
14.9%
0.416667 388
 
4.3%
0.916667 345
 
3.9%
0.833333 311
 
3.5%
0.5 310
 
3.5%
0.166667 305
 
3.4%
0.666667 292
 
3.3%
0.75 291
 
3.3%
0.083333 275
 
3.1%
Other values (37) 1187
 
13.3%
ValueCountFrequency (%)
0 3915
43.7%
0.083333 275
 
3.1%
0.090909 12
 
0.1%
0.1 6
 
0.1%
0.111111 9
 
0.1%
0.125 5
 
0.1%
0.142857 6
 
0.1%
0.166667 305
 
3.4%
0.181818 14
 
0.2%
0.2 9
 
0.1%
ValueCountFrequency (%)
1 1331
14.9%
0.916667 345
 
3.9%
0.909091 25
 
0.3%
0.9 19
 
0.2%
0.888889 28
 
0.3%
0.875 28
 
0.3%
0.857143 30
 
0.3%
0.833333 311
 
3.5%
0.818182 21
 
0.2%
0.8 18
 
0.2%

CASH_ADVANCE_FREQUENCY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1351442
Minimum0
Maximum1.5
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2024-07-31T13:52:34.739553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.222222
95-th percentile0.583333
Maximum1.5
Range1.5
Interquartile range (IQR)0.222222

Descriptive statistics

Standard deviation0.20012139
Coefficient of variation (CV)1.4807989
Kurtosis3.3347343
Mean0.1351442
Median Absolute Deviation (MAD)0
Skewness1.8286863
Sum1209.5406
Variance0.04004857
MonotonicityNot monotonic
2024-07-31T13:52:34.806702image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4628
51.7%
0.083333 1021
 
11.4%
0.166667 759
 
8.5%
0.25 578
 
6.5%
0.333333 439
 
4.9%
0.416667 273
 
3.1%
0.5 215
 
2.4%
0.583333 142
 
1.6%
0.666667 125
 
1.4%
0.090909 70
 
0.8%
Other values (44) 700
 
7.8%
ValueCountFrequency (%)
0 4628
51.7%
0.083333 1021
 
11.4%
0.090909 70
 
0.8%
0.1 39
 
0.4%
0.111111 29
 
0.3%
0.125 47
 
0.5%
0.142857 49
 
0.5%
0.166667 759
 
8.5%
0.181818 42
 
0.5%
0.2 21
 
0.2%
ValueCountFrequency (%)
1.5 1
 
< 0.1%
1.25 1
 
< 0.1%
1.166667 2
 
< 0.1%
1.142857 1
 
< 0.1%
1.125 1
 
< 0.1%
1.1 1
 
< 0.1%
1.090909 1
 
< 0.1%
1 25
0.3%
0.916667 27
0.3%
0.909091 3
 
< 0.1%

CASH_ADVANCE_TRX
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2488268
Minimum0
Maximum123
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2024-07-31T13:52:34.870437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile15
Maximum123
Range123
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.8246467
Coefficient of variation (CV)2.1006496
Kurtosis61.646862
Mean3.2488268
Median Absolute Deviation (MAD)0
Skewness5.7212982
Sum29077
Variance46.575803
MonotonicityNot monotonic
2024-07-31T13:52:34.934129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4628
51.7%
1 887
 
9.9%
2 620
 
6.9%
3 436
 
4.9%
4 384
 
4.3%
5 308
 
3.4%
6 246
 
2.7%
7 205
 
2.3%
8 171
 
1.9%
10 150
 
1.7%
Other values (55) 915
 
10.2%
ValueCountFrequency (%)
0 4628
51.7%
1 887
 
9.9%
2 620
 
6.9%
3 436
 
4.9%
4 384
 
4.3%
5 308
 
3.4%
6 246
 
2.7%
7 205
 
2.3%
8 171
 
1.9%
9 111
 
1.2%
ValueCountFrequency (%)
123 3
< 0.1%
110 1
 
< 0.1%
107 1
 
< 0.1%
93 1
 
< 0.1%
80 1
 
< 0.1%
71 1
 
< 0.1%
69 1
 
< 0.1%
63 1
 
< 0.1%
62 3
< 0.1%
61 1
 
< 0.1%

PURCHASES_TRX
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct173
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.709832
Minimum0
Maximum358
Zeros2044
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2024-07-31T13:52:34.995485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q317
95-th percentile57
Maximum358
Range358
Interquartile range (IQR)16

Descriptive statistics

Standard deviation24.857649
Coefficient of variation (CV)1.6898662
Kurtosis34.7931
Mean14.709832
Median Absolute Deviation (MAD)7
Skewness4.6306553
Sum131653
Variance617.90272
MonotonicityNot monotonic
2024-07-31T13:52:35.057773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2044
22.8%
1 667
 
7.5%
12 570
 
6.4%
2 379
 
4.2%
6 352
 
3.9%
3 314
 
3.5%
4 285
 
3.2%
7 275
 
3.1%
5 267
 
3.0%
8 267
 
3.0%
Other values (163) 3530
39.4%
ValueCountFrequency (%)
0 2044
22.8%
1 667
 
7.5%
2 379
 
4.2%
3 314
 
3.5%
4 285
 
3.2%
5 267
 
3.0%
6 352
 
3.9%
7 275
 
3.1%
8 267
 
3.0%
9 248
 
2.8%
ValueCountFrequency (%)
358 1
< 0.1%
347 1
< 0.1%
344 1
< 0.1%
309 1
< 0.1%
308 1
< 0.1%
298 1
< 0.1%
274 1
< 0.1%
273 1
< 0.1%
254 1
< 0.1%
248 2
< 0.1%

CREDIT_LIMIT
Real number (ℝ)

Distinct205
Distinct (%)2.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4494.4495
Minimum50
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2024-07-31T13:52:35.119615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile1000
Q11600
median3000
Q36500
95-th percentile12000
Maximum30000
Range29950
Interquartile range (IQR)4900

Descriptive statistics

Standard deviation3638.8157
Coefficient of variation (CV)0.80962435
Kurtosis2.8366559
Mean4494.4495
Median Absolute Deviation (MAD)1800
Skewness1.522464
Sum40220828
Variance13240980
MonotonicityNot monotonic
2024-07-31T13:52:35.185620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000 784
 
8.8%
1500 722
 
8.1%
1200 621
 
6.9%
1000 614
 
6.9%
2500 612
 
6.8%
4000 506
 
5.7%
6000 463
 
5.2%
5000 389
 
4.3%
2000 371
 
4.1%
7500 277
 
3.1%
Other values (195) 3590
40.1%
ValueCountFrequency (%)
50 1
 
< 0.1%
150 5
 
0.1%
200 3
 
< 0.1%
300 14
 
0.2%
400 3
 
< 0.1%
450 6
 
0.1%
500 121
1.4%
600 21
 
0.2%
650 1
 
< 0.1%
700 20
 
0.2%
ValueCountFrequency (%)
30000 2
 
< 0.1%
28000 1
 
< 0.1%
25000 1
 
< 0.1%
23000 2
 
< 0.1%
22500 1
 
< 0.1%
22000 1
 
< 0.1%
21500 2
 
< 0.1%
21000 2
 
< 0.1%
20500 1
 
< 0.1%
20000 10
0.1%

PAYMENTS
Real number (ℝ)

ZEROS 

Distinct8711
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1733.1439
Minimum0
Maximum50721.483
Zeros240
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2024-07-31T13:52:35.247585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile89.988924
Q1383.27617
median856.90155
Q31901.1343
95-th percentile6082.0906
Maximum50721.483
Range50721.483
Interquartile range (IQR)1517.8582

Descriptive statistics

Standard deviation2895.0638
Coefficient of variation (CV)1.6704117
Kurtosis54.770736
Mean1733.1439
Median Absolute Deviation (MAD)581.35146
Skewness5.9076198
Sum15511637
Variance8381394.2
MonotonicityNot monotonic
2024-07-31T13:52:35.309374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 240
 
2.7%
201.802084 1
 
< 0.1%
398.316441 1
 
< 0.1%
826.036748 1
 
< 0.1%
2571.573214 1
 
< 0.1%
1903.279643 1
 
< 0.1%
454.888506 1
 
< 0.1%
956.028747 1
 
< 0.1%
4560.77572 1
 
< 0.1%
1825.349955 1
 
< 0.1%
Other values (8701) 8701
97.2%
ValueCountFrequency (%)
0 240
2.7%
0.049513 1
 
< 0.1%
0.056466 1
 
< 0.1%
2.389583 1
 
< 0.1%
3.500505 1
 
< 0.1%
4.523555 1
 
< 0.1%
4.841543 1
 
< 0.1%
5.070726 1
 
< 0.1%
9.040017 1
 
< 0.1%
9.533313 1
 
< 0.1%
ValueCountFrequency (%)
50721.48336 1
< 0.1%
46930.59824 1
< 0.1%
40627.59524 1
< 0.1%
39461.9658 1
< 0.1%
39048.59762 1
< 0.1%
36066.75068 1
< 0.1%
35843.62593 1
< 0.1%
34107.07499 1
< 0.1%
33994.72785 1
< 0.1%
33486.31044 1
< 0.1%

MINIMUM_PAYMENTS
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8636
Distinct (%)> 99.9%
Missing313
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean864.20654
Minimum0.019163
Maximum76406.208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2024-07-31T13:52:35.368820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.019163
5-th percentile73.282006
Q1169.12371
median312.34395
Q3825.48546
95-th percentile2766.5633
Maximum76406.208
Range76406.188
Interquartile range (IQR)656.36175

Descriptive statistics

Standard deviation2372.4466
Coefficient of variation (CV)2.745231
Kurtosis283.98999
Mean864.20654
Median Absolute Deviation (MAD)190.3741
Skewness13.622797
Sum7464151.9
Variance5628502.9
MonotonicityNot monotonic
2024-07-31T13:52:35.511544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
299.351881 2
 
< 0.1%
150.317143 1
 
< 0.1%
271.528169 1
 
< 0.1%
6404.855484 1
 
< 0.1%
616.862544 1
 
< 0.1%
211.984193 1
 
< 0.1%
324.954747 1
 
< 0.1%
1600.26917 1
 
< 0.1%
277.546713 1
 
< 0.1%
342.28649 1
 
< 0.1%
Other values (8626) 8626
96.4%
(Missing) 313
 
3.5%
ValueCountFrequency (%)
0.019163 1
< 0.1%
0.037744 1
< 0.1%
0.05588 1
< 0.1%
0.059481 1
< 0.1%
0.117036 1
< 0.1%
0.261984 1
< 0.1%
0.311953 1
< 0.1%
0.319475 1
< 0.1%
1.113027 1
< 0.1%
1.334075 1
< 0.1%
ValueCountFrequency (%)
76406.20752 1
< 0.1%
61031.6186 1
< 0.1%
56370.04117 1
< 0.1%
50260.75947 1
< 0.1%
43132.72823 1
< 0.1%
42629.55117 1
< 0.1%
38512.12477 1
< 0.1%
31871.36379 1
< 0.1%
30528.4324 1
< 0.1%
29019.80288 1
< 0.1%

PRC_FULL_PAYMENT
Real number (ℝ)

ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15371465
Minimum0
Maximum1
Zeros5903
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2024-07-31T13:52:35.572464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.142857
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.142857

Descriptive statistics

Standard deviation0.2924992
Coefficient of variation (CV)1.9028713
Kurtosis2.4323953
Mean0.15371465
Median Absolute Deviation (MAD)0
Skewness1.9428199
Sum1375.7461
Variance0.08555578
MonotonicityNot monotonic
2024-07-31T13:52:35.633211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 5903
66.0%
1 488
 
5.5%
0.083333 426
 
4.8%
0.166667 166
 
1.9%
0.5 156
 
1.7%
0.25 156
 
1.7%
0.090909 153
 
1.7%
0.333333 134
 
1.5%
0.1 94
 
1.1%
0.2 83
 
0.9%
Other values (37) 1191
 
13.3%
ValueCountFrequency (%)
0 5903
66.0%
0.083333 426
 
4.8%
0.090909 153
 
1.7%
0.1 94
 
1.1%
0.111111 61
 
0.7%
0.125 52
 
0.6%
0.142857 54
 
0.6%
0.166667 166
 
1.9%
0.181818 75
 
0.8%
0.2 83
 
0.9%
ValueCountFrequency (%)
1 488
5.5%
0.916667 77
 
0.9%
0.909091 19
 
0.2%
0.9 16
 
0.2%
0.888889 12
 
0.1%
0.875 18
 
0.2%
0.857143 12
 
0.1%
0.833333 63
 
0.7%
0.818182 17
 
0.2%
0.8 33
 
0.4%

TENURE
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.517318
Minimum6
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.1 KiB
2024-07-31T13:52:35.679809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q112
median12
Q312
95-th percentile12
Maximum12
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3383308
Coefficient of variation (CV)0.11620159
Kurtosis7.6948232
Mean11.517318
Median Absolute Deviation (MAD)0
Skewness-2.9430173
Sum103080
Variance1.7911292
MonotonicityNot monotonic
2024-07-31T13:52:35.726149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
12 7584
84.7%
11 365
 
4.1%
10 236
 
2.6%
6 204
 
2.3%
8 196
 
2.2%
7 190
 
2.1%
9 175
 
2.0%
ValueCountFrequency (%)
6 204
 
2.3%
7 190
 
2.1%
8 196
 
2.2%
9 175
 
2.0%
10 236
 
2.6%
11 365
 
4.1%
12 7584
84.7%
ValueCountFrequency (%)
12 7584
84.7%
11 365
 
4.1%
10 236
 
2.6%
9 175
 
2.0%
8 196
 
2.2%
7 190
 
2.1%
6 204
 
2.3%

Interactions

2024-07-31T13:52:31.221888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:15.464000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:16.463923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:17.303548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:18.034990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:19.116760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:20.152582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:21.110827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:22.262148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:23.435468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:24.701291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:25.723056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:26.783231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:27.677516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:28.472545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:29.263992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:30.337144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:31.270544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-07-31T13:52:29.054852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:30.041584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:30.992239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:32.019977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:16.279310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:17.055689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:17.861461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:18.878961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:19.878263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:20.738904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:22.056232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:23.199552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:24.425664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:25.469881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:26.579202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:27.468883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:28.292125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:29.095535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:30.130481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:31.038939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:32.066247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:16.326628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:17.097662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:17.903329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:18.934850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:19.964760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:20.813946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:22.110131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:23.249189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:24.493821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:25.520051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:26.628032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:27.515700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:28.336267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:29.136283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:30.185069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:31.081173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:32.121534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:16.376129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:17.219688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:17.949486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:18.999593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:20.038932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:20.872328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:22.164575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:23.305550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:24.567805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:25.585692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:26.691440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:27.577101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:28.384929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:29.181523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:30.235300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:31.132820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:32.165995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:16.420300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:17.261999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:17.991457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:19.060747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:20.100702image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:20.929456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:22.212030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:23.368854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:24.633830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:25.655177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:26.735337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:27.629155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:28.427904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:29.223034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:30.283069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-31T13:52:31.177041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-07-31T13:52:35.771236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
BALANCEBALANCE_FREQUENCYCASH_ADVANCECASH_ADVANCE_FREQUENCYCASH_ADVANCE_TRXCREDIT_LIMITINSTALLMENTS_PURCHASESMINIMUM_PAYMENTSONEOFF_PURCHASESONEOFF_PURCHASES_FREQUENCYPAYMENTSPRC_FULL_PAYMENTPURCHASESPURCHASES_FREQUENCYPURCHASES_INSTALLMENTS_FREQUENCYPURCHASES_TRXTENURE
BALANCE1.0000.5450.5660.5440.5490.372-0.0900.9000.1460.1200.432-0.4840.006-0.145-0.144-0.0460.066
BALANCE_FREQUENCY0.5451.0000.1370.1770.1760.1060.1280.5020.1350.1590.207-0.1740.1480.2020.1520.2030.229
CASH_ADVANCE0.5660.1371.0000.9410.9520.163-0.3570.482-0.185-0.1890.257-0.266-0.385-0.454-0.378-0.408-0.113
CASH_ADVANCE_FREQUENCY0.5440.1770.9411.0000.9830.088-0.3660.456-0.179-0.1760.203-0.287-0.391-0.453-0.382-0.407-0.131
CASH_ADVANCE_TRX0.5490.1760.9520.9831.0000.097-0.3570.472-0.175-0.1740.215-0.281-0.384-0.447-0.374-0.399-0.099
CREDIT_LIMIT0.3720.1060.1630.0880.0971.0000.1230.2640.3050.2820.4490.0210.2610.1040.0470.1900.170
INSTALLMENTS_PURCHASES-0.0900.128-0.357-0.366-0.3570.1231.000-0.0520.2000.1850.2390.2760.7060.7860.9230.7840.125
MINIMUM_PAYMENTS0.9000.5020.4820.4560.4720.264-0.0521.0000.0700.0510.368-0.478-0.008-0.104-0.085-0.0250.137
ONEOFF_PURCHASES0.1460.135-0.185-0.179-0.1750.3050.2000.0701.0000.9520.3630.0490.7510.4240.1170.5900.096
ONEOFF_PURCHASES_FREQUENCY0.1200.159-0.189-0.176-0.1740.2820.1850.0510.9521.0000.3210.0610.6930.4630.1120.6070.084
PAYMENTS0.4320.2070.2570.2030.2150.4490.2390.3680.3630.3211.0000.1870.3950.1720.1210.2840.205
PRC_FULL_PAYMENT-0.484-0.174-0.266-0.287-0.2810.0210.276-0.4780.0490.0610.1871.0000.2380.2920.2590.2530.020
PURCHASES0.0060.148-0.385-0.391-0.3840.2610.706-0.0080.7510.6930.3950.2381.0000.7950.6060.8850.133
PURCHASES_FREQUENCY-0.1450.202-0.454-0.453-0.4470.1040.786-0.1040.4240.4630.1720.2920.7951.0000.8520.9240.098
PURCHASES_INSTALLMENTS_FREQUENCY-0.1440.152-0.378-0.382-0.3740.0470.923-0.0850.1170.1120.1210.2590.6060.8521.0000.7810.114
PURCHASES_TRX-0.0460.203-0.408-0.407-0.3990.1900.784-0.0250.5900.6070.2840.2530.8850.9240.7811.0000.169
TENURE0.0660.229-0.113-0.131-0.0990.1700.1250.1370.0960.0840.2050.0200.1330.0980.1140.1691.000

Missing values

2024-07-31T13:52:32.238537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-31T13:52:32.361566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-31T13:52:32.455043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUST_IDBALANCEBALANCE_FREQUENCYPURCHASESONEOFF_PURCHASESINSTALLMENTS_PURCHASESCASH_ADVANCEPURCHASES_FREQUENCYONEOFF_PURCHASES_FREQUENCYPURCHASES_INSTALLMENTS_FREQUENCYCASH_ADVANCE_FREQUENCYCASH_ADVANCE_TRXPURCHASES_TRXCREDIT_LIMITPAYMENTSMINIMUM_PAYMENTSPRC_FULL_PAYMENTTENURE
0C1000140.9007490.81818295.400.0095.400.0000000.1666670.0000000.0833330.000000021000.0201.802084139.5097870.00000012
1C100023202.4674160.9090910.000.000.006442.9454830.0000000.0000000.0000000.250000407000.04103.0325971072.3402170.22222212
2C100032495.1488621.000000773.17773.170.000.0000001.0000001.0000000.0000000.0000000127500.0622.066742627.2847870.00000012
3C100041666.6705420.6363641499.001499.000.00205.7880170.0833330.0833330.0000000.083333117500.00.000000NaN0.00000012
4C10005817.7143351.00000016.0016.000.000.0000000.0833330.0833330.0000000.000000011200.0678.334763244.7912370.00000012
5C100061809.8287511.0000001333.280.001333.280.0000000.6666670.0000000.5833330.000000081800.01400.0577702407.2460350.00000012
6C10007627.2608061.0000007091.016402.63688.380.0000001.0000001.0000001.0000000.00000006413500.06354.314328198.0658941.00000012
7C100081823.6527431.000000436.200.00436.200.0000001.0000000.0000001.0000000.0000000122300.0679.065082532.0339900.00000012
8C100091014.9264731.000000861.49661.49200.000.0000000.3333330.0833330.2500000.000000057000.0688.278568311.9634090.00000012
9C10010152.2259750.5454551281.601281.600.000.0000000.1666670.1666670.0000000.0000000311000.01164.770591100.3022620.00000012
CUST_IDBALANCEBALANCE_FREQUENCYPURCHASESONEOFF_PURCHASESINSTALLMENTS_PURCHASESCASH_ADVANCEPURCHASES_FREQUENCYONEOFF_PURCHASES_FREQUENCYPURCHASES_INSTALLMENTS_FREQUENCYCASH_ADVANCE_FREQUENCYCASH_ADVANCE_TRXPURCHASES_TRXCREDIT_LIMITPAYMENTSMINIMUM_PAYMENTSPRC_FULL_PAYMENTTENURE
8940C19181130.8385541.000000591.240.00591.240.0000001.0000000.0000000.8333330.000000061000.0475.52326282.7713201.006
8941C191825967.4752700.833333214.550.00214.558555.4093260.8333330.0000000.6666670.6666671359000.0966.202912861.9499060.006
8942C1918340.8297491.000000113.280.00113.280.0000001.0000000.0000000.8333330.000000061000.094.48882886.2831010.256
8943C191845.8717120.50000020.9020.900.000.0000000.1666670.1666670.0000000.00000001500.058.64488343.4737170.006
8944C19185193.5717220.8333331012.731012.730.000.0000000.3333330.3333330.0000000.000000024000.00.000000NaN0.006
8945C1918628.4935171.000000291.120.00291.120.0000001.0000000.0000000.8333330.000000061000.0325.59446248.8863650.506
8946C1918719.1832151.000000300.000.00300.000.0000001.0000000.0000000.8333330.000000061000.0275.861322NaN0.006
8947C1918823.3986730.833333144.400.00144.400.0000000.8333330.0000000.6666670.000000051000.081.27077582.4183690.256
8948C1918913.4575640.8333330.000.000.0036.5587780.0000000.0000000.0000000.16666720500.052.54995955.7556280.256
8949C19190372.7080750.6666671093.251093.250.00127.0400080.6666670.6666670.0000000.3333332231200.063.16540488.2889560.006